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The tumour immune microenvironment (TIME) in breast cancer is acknowledged with an increasing role in treatment response and prognosis. With a growing number of immune markers analysed, digital image analysis may facilitate broader TIME understanding, even in single-plex IHC data. To facilitate analyses of the latter an open-source image analysis pipeline, Tissue microarray MArker Quantification (TMArQ), was developed and applied to single-plex stainings for p53, CD3, CD4, CD8, CD20, CD68, FOXP3, and PD-L1 (SP142 antibody) in a 218-patient triple negative breast cancer (TNBC) cohort with complementary pathology scorings, clinicopathological, whole genome sequencing, and RNA-sequencing data. TMArQ's cell counts for analysed immune markers were on par with results from alternative methods and consistent with both estimates from human pathology review, different quantifications and classifications derived from RNA-sequencing as well as known prognostic patterns of immune response in TNBC. The digital cell counts demonstrated how immune markers are coexpressed in the TIME when considering TNBC molecular subtypes and DNA repair deficiency, and how combination of immune status with DNA repair deficiency status can improve the prognostic stratification in chemotherapy treated patients. These results underscore the value and potential of integrating TIME and specific tumour intrinsic alterations/phenotypes for the molecular understanding of TNBC., (© 2024. The Author(s).)

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing., (© 2024 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: Differential diagnosis of rhabdomyosarcoma (RMS) is challenging. Sineoculis homeobox homolog 1 (SIX1) is an oncogene involved in skeletal muscle differentiation. We compared protein expression patterns of SIX1 in RMS and its most common differential diagnoses. Methods: SIX1 immunohistochemistry in 36 RMS and in 33 tumors from seven differential diagnostic subtypes were evaluated. The fraction of SIX1 positive tumor cells was scored by three independent observers. Results: A majority (75%) of the evaluated RMS expressed SIX1 in at least 50% of tumor cells and all except one RMS had more than 25% positive tumor cells. Neuroblastoma had less than 1% SIX1 positive tumor cells. Gonadoblastoma, malignant rhabdoid tumor, and Ewing sarcoma had 10% or less positive tumor cells. Pleuropulmonary blastoma exhibited 26-50% positive tumor cells and synovial sarcoma >50% positive cells. Conclusion: SIX1 immunohistochemistry is positive in most RMS, and occasionally in some tumors within the differential diagnoses of RMS.